Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 2.281 IF 2.281
  • IF 5-year value: 2.693 IF 5-year 2.693
  • CiteScore value: 2.43 CiteScore 2.43
  • SNIP value: 1.193 SNIP 1.193
  • SJR value: 0.965 SJR 0.965
  • IPP value: 2.31 IPP 2.31
  • h5-index value: 40 h5-index 40
  • Scimago H index value: 73 Scimago H index 73
Volume 18, issue 6 | Copyright

Special issue: Landslide early warning systems: monitoring systems, rainfall...

Nat. Hazards Earth Syst. Sci., 18, 1717-1733, 2018
https://doi.org/10.5194/nhess-18-1717-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 Jun 2018

Research article | 20 Jun 2018

Adopting the I3R24 rainfall index and landslide susceptibility for the establishment of an early warning model for rainfall-induced shallow landslides

Lun-Wei Wei1,3, Chuen-Ming Huang2,3, Hongey Chen1,4, Chyi-Tyi Lee2, Chun-Chi Chi5, and Chen-Lung Chiu5 Lun-Wei Wei et al.
  • 1Department of Geosciences, National Taiwan University, Taipei City, Taiwan
  • 2Institute of Applied Geology, National Central University, Taoyuan City, Taiwan
  • 3Disaster Prevention Technology Research Center, Sinotech Engineering Consultants, INC., Taipei City, Taiwan
  • 4National Science and Technology Center for Disaster Reduction, New Taipei City, Taiwan
  • 5Central Geological Survey, MOEA, New Taipei City, Taiwan

Abstract. Rainfall-induced landslides number among the most devastating natural hazards in the world and early warning models are urgently needed to reduce losses and fatalities. Most landslide early warning systems are based on rainfall thresholds defined on the regional scale, regardless of the different landslide susceptibilities of various slopes. Here we divided slope units in southern Taiwan into three categories (high, moderate and low) according to their susceptibility. For each category, we established separate rainfall thresholds so as to provide differentiated thresholds for different degrees of susceptibility. Logistic regression (LR) analysis was performed to evaluate landslide susceptibility by using event-based landslide inventories and predisposing factors. Analysis of rainfall patterns of 941 landslide cases gathered from field investigation led to the recognition that 3h mean rainfall intensity (I3) is a key rainfall index for rainfall of short duration but high intensity; in contrast, 24h accumulated rainfall (R24) was recognized as a key rainfall index for rainfall of long duration but low intensity. Thus, the I3R24 rainfall index was used to establish rainfall thresholds in this study. Finally, an early warning model is proposed by setting alert levels including yellow (advisory), orange (watch) and red (warning) according to a hazard matrix. These differentiated thresholds and alert levels can provide essential information for local governments to use in deciding whether to evacuate residents.

Publications Copernicus
Special issue
Download
Short summary
The difference in susceptibility might lead to a difference in warning threshold for rainfall-induced landslides. Here we divided slope units into three susceptibility levels and established their thresholds separately. It was found that the threshold values gradually increased as the susceptibility decreased for the same alert level. This showed that classifying susceptibility and establishing thresholds separately might provide refined thresholds for disaster prevention.
The difference in susceptibility might lead to a difference in warning threshold for...
Citation
Share